2004 NIH Building on the BIRN Bruce Rosen, MD PhD Randy Gollub, MD PhD Steve Pieper, PhD ...

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2004 NIH Building on the BIRN

Bruce Rosen, MD PhD

Randy Gollub, MD PhD

Steve Pieper, PhD

http://www.nbirn.net

Morphometry BIRN

What is the Morphometry BIRN? (B. Rosen)

Scientific Background and Significance (R. Gollub)

BIRN Advantages in Morphometry (S. Pieper)

The BIRN Advantage

OUTLINE: Morphometry BIRN

Overall Goal:

Develop capability to analyze and mine data

acquired at multiple sites using processing and

visualization tools developed at multiple sites

Morphometry BIRN

Overall Goal:

Develop capability to analyze and mine data acquired at multiple sites

using processing and visualization tools developed at multiple sites

Context: Human Brain MR Based Morphometry

Initial Application: Alzheimer’s, Depression, Ageing Brain

Participants: BWH, MGH, Duke, UC Los Angeles,

UC San Diego, Johns Hopkins, UC

Irvine,

Washington University

Morphometry BIRN

Human Data Protection

Multi-site data

acquisition

Data Upload

Integration and Application of

Processing Tools

Human Imaging

Database

Morphometry BIRN: Flowchart

Simplified diagram

of building blocks

Multi-site data

acquisition

Data Upload

Processing Tools

Human Imaging

Database

Simplified diagram

of building blocks

Raw data De-faced data

• De-facing: automated de-facing without brain removal• Pipeline: image formats, BIRN ID generation, defacing, QA, upload

Accomplishment: Developed a robust automated methods for

bulk MRI de-identification and upload to database(diverse inputs, sharable outputs, common

package)

De-identification and Upload Pipeline*

• UCSD (fMRI): A. Bischoff, C.Notestine, B.

Ozyurt , S. Morris, G.G. Brown

• MGH (NMR): B. Fischl

• BWH (SPL): S. Pieper

• UCI: D. Wei

• Duke: B. Boyd

*See demo

Morphometry BIRN: Reality

Multi-site data

acquisition

Data Upload

Processing Tools

Human Imaging

Database

Simplified diagram

of building blocks

Multi-site Structural MRI Data Acquisition & Calibration

Methods: common acquisition protocol, distortion correction, evaluation by scanning human phantoms multiple times at all sites

•MGH (NMR): J. Jovicich, A. Dale, D. Greve, E. Haley

•BWH (SPL): S. Pieper•UCI: D. Keator•UCSD (fMRI): G. Brown •Duke University (NIRL): J. MacFall CorrectedUncorrected

Image intensity variability onsame subject scanned at 4 sites

Accomplishment: develop acquisition & calibration protocols that improve reproducibility, within- and across-sites

Multi-site data

acquisition

Data Upload

Processing Tools

Human Imaging

Database

Simplified diagram

of building blocks

Shared Tools for Data Analysis

• Freesurfer MGH

• Slicer BWH

• LONI Pipeline UCLA

• LDDMM Johns Hopkins

Morphometry BIRN

Integration and Application of Processing Tools

Various projects driving developments:

• Multi-site Imaging Research in Analysis of Depression*† • Data from one site processed with tools of multiple sites

• Multi-site Morphometry in Analysis of Alzheimer’s Disease *†• Data from multiple sites processed with tools of one site

• Semi-Automated Shape Analysis Project †• Data from BIRN sites processed with tools of various sites

* Demo at BIRN Toolbox’s session (12:00-3:00pm)† Poster available with more details

Morphometry BIRN

Projects driving developments:

MIRIAD Project: Overview

DukeArchives

UCLAAIR Registration

and Lobar Analysis

BWHIntensity Normalizationand EM Segmentation

DukeClinical Analysis

BWH Probabilistic Atlas

(one time transfer)

UCSDSupercomputing

Goal: analyze legacy data using automated lobar

segmentation (UCLA) and cortical/subcortical

segmentations (BWH)

MIRIAD Project: Accomplishments

Segmentation Duke BIRN-MIRIAD

Item (semi-automated) (fully-automated)

# of tissue classes 3 (Fig1) 23 (Fig2)

Time for 200 brains 400 hours 1 hour

Time for 200 lobe & 250 hours all lobes (Fig3) and 27 regional analysis regions included above

Improved computational capabilities

AD Project: Overview

MGH Segmentation

Multi-Site Data Acquisition

De-identification and upload

SRB UCSD

BWH/MGH

Multi-site DataQueries and

Statistics

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CVLT Discriminability Score

Left Hippocampal Volume

BWH/MGH and UCSD Data

Visualization and Scientific Search with

3DSlicer & Query Atlas

AD Project: Accomplishments

• Data sharing:• Successfully tested Deidentification and Upload Pipeline (DUP)

• Integration of data

• Common database schemas for clinical and derived morphometry data at different sites Human Imaging Database (HID)

• Mediated queries that interrogate databases at two sites

• Integration of processing tools

• MGH subcortical segmentation completed on UCSD data

• Statistical tools through the BIRN Portal and HID Query Interface

• Data visualization and interpretation using 3DSlicer and Query Atlas

SASHA Project: Overview

MGH Segmentation

Data DonorSites

De-identificationAnd upload

JHUShape Analysis

of Segmented Structures

BWHVisualization

Goal: comparison and quantification of structures’

shape and volumetric differences across patient

populations

UCSDSupercomputing

SASHA Project: Accomplishments

Data: 46 hippocampus data sets (2070 comparisons) Each LDDMM comparison takes about 3 to 8 hours

Large Deformation Diffeomorphic Metric Mapping (LDDMM) using the TeraGrid

Improved computational capabilities

Single PC TeraGrid 1 comparison ~431 days

60 comparisons simultaneously ~7 days

Morphometry BIRN: Future

Calibration: Expand imaging modalities,correction methods, refine protocols

Analysis & Visualization: continue integration development, improve automaticity, support new imaging modalities

Computational Informatics: database interoperability, support genomics, support new image data, grid enable

Utilization: propagate widespread utilization of infrastructure, add new sites to testbed

Why are we here today?

WE NEED FEEDBACK FROM YOU:

Which developments are useful for your projects?

Which developments are we missing?

How can we build better bridges to your projects?

Challenges Technical

• Integration of disparate sources (data and software)

• Processing and handling of large datasets

• Federation of databases in compliance with HIPAA

• Quality control

• Audit and versioning requirements

• Accessing legacy data

• Project coordination and knowledge distillation

Sociological• Encouraging collaboration

• Intellectual Property issues (data & software sharing)

• Authorship

Metrics for Success

Adopted for use by increasing numbers of experts

Sharing of tools and infrastructure with scientific community

Creation and maintenance of a valuable image archive that supports on-going research

Peer reviewed publications in scientific and technical journals

Presentations at national and international scientific meetings

Professional advancement of key personnel linked to success of project

AD Project: Accomplishments

• Successfully tested DUP to share data across sites following federal HIPAA guidelines

• Established common database schemas for clinical and derived morphometry data at different sites (HID)

• Enabled mediated queries that interrogate databases at two sites

• Successfully tested integration of tools for common analysis and data mining

• MGH Subcortical segmentation completed on UCSD data

• Univariate and bivariate statistical tools through the BIRN Portal and HID Query Interface

• Data visualization and intelligent scientific search based on anatomical labels using 3DSlicer and Query Atlas

MIRIAD Project: Accomplishments

• 50 depressed, 50 controls, imaged at baseline and 2 years• Parietal lobe smaller in depressed (p < 0.02)• In subjects responding to therapy:

• Temporal lobe smaller (p < 0.08)• Frontal lobe was not smaller (p < 0.6)

2004 NIH Building on the BIRN Bruce Rosen, MD PhD Randy Gollub, MD PhD Steve Pieper, PhD ...

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